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182 articles for “Prediction”
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Machine Learning Approaches for Customer Churn Prediction in Telecommunications
Abstract: Customer churn prediction is a critical challenge for companies in the telecommunications industry. The competition is intense, and acquiring new customers can be costly. Predicting when existing customers might leave (churn) helps companies take proactive measures to retain them, reducing churn rates and costs. By analyzing historical customer data, including demographics, service usage, billing, and customer service interactions, this study aims to identify customers at risk of leaving their current …
Published in International Journal of Telecommunications & Emerging Technologies · Vol. 10, Issue 2, 2024 · pp. 1–7 Read article
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The Role of Artificial Intelligence in ICU Patient Monitoring and Predictive Analytics: A Prospective Cohort Study
Abstract: Background: Intensive Care Units (ICUs) face challenges in effectively monitoring critically ill patients. Traditional monitoring methods may not detect subtle signs of deterioration in time, leading to worsened outcomes. Artificial Intelligence (AI)-driven monitoring systems have emerged as a potential solution, promising improved predictive capabilities and patient outcomes. Objective: This study aimed to evaluate the impact of AI-based monitoring systems on ICU patient outcomes, focusing on mortality rates, length of ICU …
Published in International Journal of Nursing Critical Care · Vol. 11, Issue 1, 2025 Read article
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Comparison of predictive ability of stroke-associated pneumonia risk scores in acute hemorrhagic stroke
Abstract: Objective: To determine the best scoring system among 5 scores (Pneumonia score, PANTHERIS, ISAN score, A2DS2, ACDD4) for prediction of pneumonia after acute haemorrhagic stroke in Stroke Centre. Subjects and Methods: We carried out retrospective study of 508 patients with acute haemorrhagic stroke admitted to Department of Neurology of Pyongyang medical university from January 1, 2018 to August 1, 2022. Demographic and clinical traits of patients were collected and analysed. …
Published in International Journal of Nursing Critical Care · Vol. 10, Issue 2, 2024 · pp. 12–19 Read article
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Prediction of Perineal Trauma During Childbirth By Assessment of Striae Gravidarum Score
Abstract: Perineal trauma is a common complication during vaginal childbirth, often resulting in short- and long-term maternal morbidity. Early prediction of women at risk can aid in preventive strategies and improve maternal outcomes. Striae gravidarum, commonly known as stretch marks, are skin changes that occur during pregnancy due to hormonal, mechanical, and genetic factors. Recent studies have suggested that the presence and severity of striae gravidarum may be associated with the …
Published in International Journal of Obstetrics, Perinatal and Neonatal Nursing · Vol. 11, Issue 1, 2025 · pp. 11–23 Read article
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Predictive Models for Drinking Water Quality: Leveraging Machine Learning for Improved Safety and Efficiency
Abstract: Quality of drinking water is one of the critical determinants of public health, leading to severe environmental and health-related issues when poor. The paper examines the integration of machine learning techniques that could improve accuracy and efficiency in assessing drinking water quality. The traditional techniques of monitoring drinking water are resource-intensive yet lack timely insight into contamination events. In this scenario, an evaluation of the performance of ML models such …
Published in International Journal of Microelectronics and Digital integrated circuits · Vol. 11, Issue 1, 2025 Read article
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AI-Driven Predictive Maintenance in Smart Homes: Enhancing Efficiency and Reliability
Abstract: The convergence of Artificial Intelligence (AI) and the Internet of Things (IoT) has transformed smart homes into intelligent, self-regulating environments. Despite these advancements, ensuring the long-term reliability and efficiency of interconnected devices remains a complex challenge due to device heterogeneity, dynamic usage patterns, and continuous data streams. This study investigates AI-driven predictive maintenance (PdM) as a proactive strategy for detecting early signs of equipment degradation in residential settings. The proposed …
Published in International Journal of Distributed Computing and Technology · Vol. 12, Issue 1, 2026 · pp. 1–6 Read article
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AI-Integrated Precision Agriculture System for Fertilizer Optimization, Multimodal Crop Yield Prediction
Abstract: Agriculture is rapidly transforming into a data-driven ecosystem supported by advances in artificial intelligence (AI), Internet of Things (IoT) sensor networks, and satellite remote sensing technologies. These innovations enable precision-driven decision-making aimed at improving productivity while minimizing environmental impact. This paper presents an AI-integrated precision agriculture system designed to optimize fertilizer usage and enhance crop yield prediction accuracy through a robust multimodal deep learning framework. The proposed approach integrates heterogeneous …
Published in International Journal of Plant Biotechnology · Vol. 12, Issue 1, 2026 · pp. 11–17 Read article
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Unveiling Employee Attrition: Harnessing Predictive Modeling for Proactive Workforce Management
Abstract: This study aims to predict employee turnover and identify organizational factors contributing to attrition by employing predictive analytics techniques. Employee attrition, which refers to the loss of employees without immediate replacements, leads to reduced overall workforce productivity. The study evaluates different machine learning techniques, such as gradient boosting, random forest, and logistic regression, to identify employees who are most likely to leave, and uses SPSS software to ensure the reliability …
Published in International Journal of Image Processing and Pattern Recognition · Vol. 10, Issue 2, 2024 · pp. 1–10 Read article
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Estimation of Crop Yield Prediction Using Machine Learning Technique.
Abstract: Agriculture serves as the primary source of livelihood for a significant portion of India's population, but it faces a myriad of challenges, including climate variability, soil fertility issues, and reliance on traditional farming practices. To address these challenges, researchers are increasingly turning to machine learning (ML) methodologies to predict crop yields more accurately. Studies have delved into the effectiveness of various ML algorithms such as Random Forest, Gaussian Process Regression, …
Published in International Journal of Agrochemistry · Vol. 10, Issue 1, 2024 · pp. 7–12 Read article
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Advancements in Predictive Maintenance: A Review of Data-Driven Approaches, Machine Learning, and AI in Diverse Industrial Landscapes
Abstract: The Internet of Things (IoT), the use of machine learning (ML), and artificial intelligence (AI)-driven advances in predictive maintenance are the key topics of this study. With the goal of offering a thorough grasp of existing approaches and potential future developments, it analyzes applications in predictive maintenance in many sectors. Predictive maintenance approaches are the major topic of discussion, with an emphasis on their advantages, disadvantages, difficulties, and prospects. Additionally, …
Published in International Journal of Computer Aided Manufacturing · Vol. 10, Issue 2, 2024 · pp. 19–32 Read article
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Optimized Fault Current Prediction for DFIG-Based Wind Turbines Using Type-I Fuzzy Logic Controller
Abstract: The increasing integration of Doubly-Fed Induction Generator (DFIG)-based wind turbines into modern power grids requires robust fault detection and prediction mechanisms to ensure stable and reliable operation. However, accurately predicting fault currents in such systems is challenging due to the nonlinear dynamics involved. This study presents an optimized fault current prediction framework using a Type-I Fuzzy Logic Controller (FLC) to address these challenges. The proposed approach effectively models the uncertainties …
Published in International Journal of Analog Integrated Circuits · Vol. 10, Issue 2, 2024 · pp. 41–50 Read article
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Ethereum Price Prediction Using Extra Trees and Random Forest Regressors
Abstract: The Ethereum market is famous for its unpredictability and the possibility of significant profits, presenting both opportunities and obstacles for investors and stakeholders. Given the dynamic nature of this ecosystem, accurate prediction of Ethereum’s price movements is essential for informed decision-making. In this project, we propose an innovative approach that leverages ensemble learning techniques, specifically extra trees and random forest regressors, for Ethereum price prediction. By harnessing a diverse dataset …
Published in International Journal of Distributed Computing and Technology · Vol. 10, Issue 2, 2024 · pp. 9–25 Read article
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Predicting Mortality in Burn Patients in A Tertiary CareSetting Using the Revised Baux Score
Abstract: Burns are a significant risk factor contributing to mortality and morbidity, especially in underdevelopednations. While innovations in burn patient management have considerably reduced mortality rates indeveloped countries, the fatality rates remain unacceptably high in underdeveloped regions where theseadvancements have not been widely adopted or implemented. Accurate prediction of outcomes forsevere burn patients at the time of presentation is crucial for guiding clinical decisions and improvingpatient management. In developed countries, several …
Published in International Journal of Chemical Separation Technology · Vol. 10, Issue 1, 2024 · pp. 27–32 Read article
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An Integrated Machine Learning and IoT-Based Framework for Predictive Urban Water Demand Management and Distribution Optimization
Abstract: Abstract Urban water resource management has become increasingly complex due to rapid urbanization, population growth, climate variability, and aging infrastructure. Conventional water management approaches are often reactive, inefficient, and incapable of handling real-time fluctuations in demand and supply. This study proposes an integrated framework that combines machine learning (ML) techniques with Internet of Things (IoT)-based real-time monitoring to enhance water demand prediction and optimize distribution systems in urban environments. The …
Published in International Journal of Water Resources Engineering · Vol. 12, Issue 1, 2026 Read article
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Integrating Cellular Automata for Predicting Urban Expansion: A Case Study of Bavdhan, Sus, and Mahalunge
Abstract: Rapid Urban Expansion in the metropolitan cities like Pune, result in indiscriminate land use changes transforming natural landscapes. Such haphazard spatial development poses a risk to depletions of resources along with the disarray of the ecological system. A proactive approach to predicting and analyzing such changes is necessary in promoting the well-managed growth of cities. For the complex nature of urban growth, simulation method is used for analyzing spatial patterns, …
Published in International Journal of Town Planning and Management · Vol. 12, Issue 1, 2026 · pp. 51–62 Read article
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Predicting Water Distribution Pipe Thermal Abnormalities Caused by Wildfire
Abstract: The paper covers the temperature thermal control of the water distribution system by predicting the temperature exceeding the safe level at pipes' locations and shutting off the water access to the main distribution pipe. The advantages of predictive thermal control over setting the maximum allowed temperature are provided. The prediction uses a transient heat conduction model with an error function approximation for arbitrary distance from the surface. It is provided …
Published in International Journal of Environmental Chemistry · Vol. 11, Issue 2, 2025 · pp. 89–97 Read article
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Predicting User Engagement on Social Media: A Comparative Study of Machine Learning-based Modeling Approaches
Abstract: Abstract Even while mobile social apps are becoming more and more important in people's daily lives, little is known about the elements that motivate users to interact with these apps. The goal of this work is to analyze and define the transition patterns of individual users' in-app actions as a temporally changing action graph. According to our investigation, action graphs are a useful tool for characterizing user behaviour patterns and …
Published in International Journal of Computer Aided Manufacturing · Vol. 11, Issue 2, 2025 · pp. 41–62 Read article
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A Review of AI and Machine Learning Techniques in Dew Point Pressure Prediction for Gas Condensate Systems
Abstract: Condensate gas reservoirs pose considerable management challenges due to their intricate phase behavior and the critical need for accurate predictions of dew point pressure (PDew). A precise determination of PDew is vital for optimizing production strategies, estimating reserves, and planning enhanced oil recovery operations. Traditional methods for PDew determination often involve experimental analyses that can be both costly and time-consuming, highlighting the necessity for alternative predictive models. This review consolidates …
Published in International Journal of Chemical Engineering and Processing · Vol. 11, Issue 1, 2025 Read article
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A Role of Machine Learning for Students' Academic Success Prediction
Abstract: This research paper aims to identify early predictions of students’ academic success with the help of different indicators. One of the crucial parts of the study is identifying additional indicators that play an essential role in defining students' academic success. Early prediction of success will be helpful for various stakeholders such as students, parents, educators/teachers, institutions, universities, and the government. Machine Learning is a part of Artificial Intelligence (AI) and …
Published in International Journal of Computer Aided Manufacturing · Vol. 10, Issue 1, 2024 · pp. 1–6 Read article
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A Machine Learning-Based Framework for Urban Traffic Congestion Prediction and Control Using Intelligent Transportation Systems
Abstract: Abstract Traffic congestion remains a critical challenge in rapidly expanding urban areas, causing increased travel delays, excessive fuel consumption, environmental degradation, and economic inefficiencies. Traditional traffic management approaches are often insufficient, as they lack the ability to respond dynamically to continuously changing traffic conditions. This study proposes a novel data-driven framework for the prediction and management of traffic congestion using Intelligent Transportation Systems (ITS). The methodology integrates real-time and historical …
Published in International Journal of Transportation Engineering and Traffic System · Vol. 12, Issue 1, 2026 · pp. 12–17 Read article